We have proposed a novel MMDL method based on DCCA that has the ability to predict CRT response using only echocardiography data but at the same time taking advantage of the implicit relationship between CMR and echocardiography. To the best of our knowledge, this is the first time that multimodal imaging data has been used directly for CRT response prediction. Previous works have mainly focused on imaging-derived parameters or atlas-based approaches, where manual input is required.
Compared to our previous work [1]}, the proposed approach allows CRT response to be predicted using only the widely available and cheap modality of echocardiography. Some previous work has proposed a multimodal ML model that exploits echocardiography alone at inference time [2]}. Compared to this work, the proposed approach makes direct use of 2D CMR and 2D echocardiography imaging data, rather than the results of motion tracking algorithms, which reduces the complexity of building the spatio-temporal model and eliminates the effect of potential errors in the motion tracking. In addition, the proposed framework is fully automated while the framework proposed in [2]} required manual delineations of the CMR and echocardiography data.
